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Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors.

Jitesh Ranjan¹, Karali Patra¹, Tibor Szalay²

¹Department of Mechanical Engineering, Indian Institute of Technology Patna, Patna-801103, India.

Sensors (Basel, Switzerland)

|February 13, 2020

Summary

This summary is machine-generated.

This study explores using vibration and cutting force signals for micro-drilling tool condition monitoring. Combining wavelet features from vibration signals best predicted micro-hole quality, improving manufacturing processes.

Keywords:

adaptive neuro fuzzy inference system cutting force micro drilling vibration wavelet packet

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Area of Science:

Manufacturing Engineering
Materials Science
Mechanical Engineering

Background:

Micro-hole drilling is crucial across various industries, necessitating effective tool wear and breakage monitoring.
Ensuring high hole quality and productivity in micro-drilling relies on real-time tool condition assessment.

Purpose of the Study:

To evaluate the effectiveness of vibration and cutting force signals for tool condition monitoring in micro-drilling.
To determine optimal strategies for predicting micro-hole quality using sensor data.
To develop an adaptive neuro fuzzy inference system (ANFIS) model for hole quality prediction.

Main Methods:

Utilized vibration and cutting force signals, individually and in combination, during micro-drilling operations (0.4 mm drills).
Extracted time domain and wavelet packet features from sensor signals.
Developed and applied an adaptive neuro fuzzy inference system (ANFIS) model for hole quality prediction.

Main Results:

A combination of sensor features in the wavelet domain of the vibration signal yielded the best prediction of hole quality.
The ANFIS model demonstrated good agreement between predicted and experimental results.
Identified vibration signal's wavelet features as highly effective for monitoring tool condition and predicting hole quality.

Conclusions:

Combining sensor features, particularly in the wavelet domain of vibration signals, is a superior strategy for micro-drilling tool condition monitoring.
The developed ANFIS model accurately predicts micro-hole quality, enabling process optimization.
Effective tool condition monitoring is essential for enhancing micro-drilling productivity and component quality.